Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab

Hongyan Li; Bingjian Feng; Alexander Miron; Xiaoqing Chen; Jonathan Beesley; Emmanuella Bimeh; Daniel Barrowdale; Esther M John; Mary B Daly; Irene L Andrulis; Saundra S Buys; Peter Kraft; kConFab investigators; Heather Thorne; Georgia Chenevix-Trench; Melissa C Southey; Antonis C Antoniou; Paul A James; Mary Beth Terry; Kelly-Anne Phillips; John L Hopper; Gillian Mitchell; David E Goldgar

doi:10.1038/gim.2016.43

Breast cancer risk prediction using a polygenic risk score in the familial setting: a prospective study from the Breast Cancer Family Registry and kConFab

Genet Med. 2017 Jan;19(1):30-35. doi: 10.1038/gim.2016.43. Epub 2016 May 12.

Authors

Hongyan Li¹, Bingjian Feng², Alexander Miron^{3

4}, Xiaoqing Chen⁵, Jonathan Beesley⁵, Emmanuella Bimeh⁶, Daniel Barrowdale⁷, Esther M John^{8

9}, Mary B Daly¹⁰, Irene L Andrulis¹¹, Saundra S Buys¹², Peter Kraft¹³; kConFab investigators; Heather Thorne¹⁴, Georgia Chenevix-Trench⁵, Melissa C Southey¹⁵, Antonis C Antoniou⁷, Paul A James^{16

17}, Mary Beth Terry^{18

19}, Kelly-Anne Phillips^{16

17

20}, John L Hopper²⁰, Gillian Mitchell^{16

17}, David E Goldgar^{1

2}

Affiliations

¹ Cancer Control and Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.
² Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA.
³ Dana Farber Cancer Institute, Boston, Massachusetts, USA.
⁴ Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA.
⁵ Cancer Division, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
⁶ Division of Family and Preventive Medicine, University of Utah, Salt Lake City, Utah, USA.
⁷ Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
⁸ Cancer Prevention Institute of California, Fremont, California, USA.
⁹ Department of Health Research and Policy (Epidemiology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, USA.
¹⁰ Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA.
¹¹ Lunenfeld-Tanenbaum Research Institute, Department of Molecular Genetics, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada.
¹² Department of Medicine, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA.
¹³ Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
¹⁴ Research Division, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.
¹⁵ Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Australia.
¹⁶ Familial Cancer Centre, Peter MacCallum Cancer Centre, East Melbourne, Victoria, Australia.
¹⁷ Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
¹⁸ Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA.
¹⁹ Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA.
²⁰ Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, Melbourne School of Population Health, University of Melbourne, Melbourne, Victoria, Australia.

Abstract

Purpose: This study examined the utility of sets of single-nucleotide polymorphisms (SNPs) in familial but non-BRCA-associated breast cancer (BC).

Methods: We derived a polygenic risk score (PRS) based on 24 known BC risk SNPs for 4,365 women from the Breast Cancer Family Registry and Kathleen Cuningham Consortium Foundation for Research into Familial Breast Cancer familial BC cohorts. We compared scores for women based on cancer status at baseline; 2,599 women unaffected at enrollment were followed-up for an average of 7.4 years. Cox proportional hazards regression was used to analyze the association of PRS with BC risk. The BOADICEA risk prediction algorithm was used to measure risk based on family history alone.

Results: The mean PRS at baseline was 2.25 (SD, 0.35) for affected women and was 2.17 (SD, 0.35) for unaffected women from combined cohorts (P < 10^-6). During follow-up, 205 BC cases occurred. The hazard ratios for continuous PRS (per SD) and upper versus lower quintiles were 1.38 (95% confidence interval: 1.22-1.56) and 3.18 (95% confidence interval: 1.84-5.23) respectively. Based on their PRS-based predicted risk, management for up to 23% of women could be altered.

Conclusion: Including BC-associated SNPs in risk assessment can provide more accurate risk prediction than family history alone and can influence recommendations for cancer screening and prevention modalities for high-risk women.Genet Med 19 1, 30-35.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Adult
Aged
Breast Neoplasms / epidemiology
Breast Neoplasms / genetics*
Breast Neoplasms / pathology
Early Detection of Cancer*
Female
Genetic Predisposition to Disease*
Humans
Middle Aged
Multifactorial Inheritance / genetics*
Polymorphism, Single Nucleotide
Registries
Risk Assessment
Risk Factors

Supplementary concepts

Breast Cancer, Familial

Abstract

Publication types

MeSH terms

Supplementary concepts

Grants and funding